Automatic stent detection

ABSTRACT

This invention relates generally to the detection of objects, such as stents, within intraluminal images using principal component analysis and/or regional covariance descriptors. In certain aspects, a training set of pre-defined intraluminal images known to contain an object is generated. The principal components of the training set can be calculated in order to form an object space. An unknown input intraluminal image can be obtained and projected onto the object space. From the projection, the object can be detected within the input intraluminal image. In another embodiment, a covariance matrix is formed for each pre-defined intraluminal image known to contain an object. An unknown input intraluminal image is obtained and a covariance matrix is computed for the input intraluminal image. The covariances of the input image and each image of the training set are compared in order to detect the presence of the object within the input intraluminal image.

RELATED APPLICATION

This application claims the benefit of and priority to U.S. ProvisionalNo. 61/710,429, filed Oct. 5, 2012, which is incorporated by referencein its entirety.

TECHNICAL FIELD

This invention generally relates to the automatic detection of stents inintraluminal imaging.

BACKGROUND

Tomographic imaging is a signal acquisition and processing technologythat allows for high-resolution cross-sectional imaging in biologicalsystems. Tomographic imaging systems include, for example, opticalcoherence tomography systems, ultrasound imaging systems, and computedtomography. Tomographic imaging is particularly well-suited for imagingthe subsurface of a vessel or lumen within the body, such as a bloodvessel, using probes disposed within a catheter through a minimallyinvasive procedure.

Typical tomographic imaging catheters consist of an imaging core thatrotates and moves longitudinally through a blood vessel, while recordingan image video loop of the vessel. The motion results in a 3D dataset,where each frame provides a 360 degree slice of the vessel at differentlongitudinal section. These frames provide cardiologists with invaluableinformation such as the location and severity of the stenosis in apatient, the presence of vulnerable plagues, and changes in the diseaseover time. The information also assists in determining the appropriatetreatment plan for the patient, such as drug therapy, stent placement,angioplasty, or bypass surgery.

One of the most common analyses performed is the placement andapposition of stents. A stent is a small, typically meshed or slotted,tube-like structure made of a metal or polymer that is inserted into theblood vessel to hold vessel open and keep it from occluding and providesa framework for arterial lesions that are likely to embolize afterballoon angioplasty. During placement, the stent should be placed inparallel within the vessel and contact the vessel wall. Apposition of acoronary artery stent is the term for how well stent lies against thewall of the artery. When the stent as placed does not mesh completelyagainst the blood vessel, the stent is in ‘incomplete apposition’.Incomplete apposition may raise the risk of a subsequent blockage orthrombus because of blood pooling or stagnating in the dead spacebetween the stent and the coronary artery wall. Therefore, it iscritical to verify that the stent is properly employed.

In order to identify and measure stent opposition in intravascularimages, a cardiologist typically has to manually locate the stentstruts, which are the framework of the stent visible in the tomographicimage. Generally, identification of at least two stent struts isrequired to determine stent apposition. This process can be a very timeconsuming and is prone to user error.

SUMMARY

This invention generally improves the ability of user of a tomographicimaging system to quickly assess a deployed stent by providing a methodfor detecting the stent location. Through use of the image processingtechniques, the stent locations for all frames or a subset of frames ina recorded dataset for an imaging run are detected and provided to theuser. The resulting stent detections may be displayed on the tomographicimage, the image longitudinal display (ILD) or displayed on 3-Dreconstructions of the vessel. This advantageously eliminates the needfor the user to manually locate the stent struts in order to quantifythe apposition. Moreover, automatically detecting stents reduces errorassociated with manual detection and provides a more reliable means todetect and remedy mal-apposed stents.

Tomographic imaging systems suitable for use in aspects of the inventioninclude any tomographic cross-sectional imaging system capable ofobtaining intraluminal images, such as optical coherence tomographysystems, ultrasound imaging systems and combined OCT-ultrasound imagingsystems. Intraluminal images are typically intravascular images, butalso include any image taken within a biological lumen, such as anintestinal lumen.

This invention relates to computing systems and methods forcomputer-assisted detection of a stent, a stent strut, or a portion ofthe stent, and can also be used to detect other objects withinintraluminal images such as tissue or a guidewire. Objects areidentified based on the locations in the polar coordinate system usingdata obtained from one-dimensional, two dimensional or three-dimensionalimages. Once stent struts are identified, measurements of the stentapposition or coverage relative to the lumen border can be easilycomputed.

In one aspect, a set of pre-defined intraluminal images that are knownto display a object are generated to train a processor to identify orrecognize the object in intraluminal images unknown to contain theobject, for example, input intraluminal images of a patient undergoingan OCT examination. For example, in this step, a set of pre-definedintraluminal data images can include a plurality of intraluminal imagesknown to display a stent strut so that a processor can be trained toidentify the stent strut. After a training set of the pre-definedintraluminal images is generated, the principal components for the setcan be computed to create an object space for the object. The principalcomponents for the object can be stored and used to detect the object ininput intraluminal images that are unknown to contain the object. Byprojecting the input intraluminal image onto the object space, theobject can be detected within the input intraluminal image.

In certain embodiments, after the input intraluminal image is projectedonto the object space, the error, for example, the Euclidean distance,between the input intraluminal image and the object space image isdetermined. A small error can constitute a positive detection of theobject within input intraluminal image. The image can then bepost-processed to identify or highlight the detected object within theinput intraluminal image. Post-processing can also include removingfalse object detections from the input intraluminal image.

In some embodiments, at least two sets of pre-defined intraluminalimages known to display different objects are generated, for example, aset of pre-defined intraluminal images known to display stents and a setof pre-defined intraluminal images known to display tissue. Theprincipal components for each set can be computed to generate an objectspace for each object. An input intraluminal image unknown to displayeither object is projected onto each object space and the objects aredetected within the input intraluminal images. The objects can bedetected by calculating an error between the input intraluminal imageand each object space. The object space that most accurately representsthe input intraluminal image, for example, has the smallest error, isindicative of a positive detection of the corresponding object to theobject space. The object space with the larger error can be indicativeof a negative detection for its corresponding object. Thisadvantageously increases the accuracy of the detection because insteadof detecting based on error alone, detection is based on the combinationof error and a comparison of the errors.

In another aspect, an object, such as stent, can be detected within aninput intraluminal image by generating a training set of intraluminalimages of an object, where each image is defined by one or morefeatures. A covariance matrix can be computed for a feature within eachpre-defined intraluminal image of the training set. The covariance for afeature within the input intraluminal image can be calculated andcompared to the covariances of the training set. From the comparison,the object can be detected within the input intraluminal image. Incertain aspects, the feature can be the Cartesian coordinates of apixel, the intensity at each pixel, or the first and second orderderivatives of the image in the x and y direction.

Other and further aspects and features of the invention will be evidentfrom the following detailed description and accompanying drawings, whichare intended to illustrate, not limit, the invention.

BRIEF DESCRIPTION OF DRAWINGS

FIG. 1 is a perspective view of a vessel.

FIG. 2 is a cross sectional view of the vessel shown in FIG. 1.

FIG. 3 is a diagram of components of an optical coherence tomography(OCT) system.

FIG. 4 is a diagram of the imaging engine shown in FIG. 3.

FIG. 5 is a diagram of a light path in an OCT system of certainembodiments of the invention.

FIG. 6 is a patient interface module of an OCT system.

FIG. 7 is an illustration of the motion of parts of an imaging catheteraccording to certain embodiments of the invention.

FIG. 8 shows an array of A scan lines of a three-dimensional imagingsystem according to certain embodiments of the invention.

FIG. 9 shows the positioning of A scans with in a vessel.

FIG. 10 illustrates a set of A scans used to compose a B scan accordingto certain embodiments of the invention.

FIG. 11 shows the set of A scans shown in FIG. 10 within a cross sectionof a vessel.

FIG. 12 shows a sample OCT B-Scan image calculated from 660 A-scans.

FIG. 13 shows a scan-converted OCT image from the B-scan of FIG. 12.

FIG. 14 depicts a basic flow chart for principal component analysis forstent detection.

FIG. 15 depicts an example OCT B-Scan with stent struts detectedfollowing the principal component analysis outlined in FIG. 14.

FIG. 16 depicts the error of projected data using stent and tissueprincipal components.

FIG. 17 depicts the resulting stent detections in scan-converted imageof FIG. 15.

FIG. 18 depicts the tissue error and stent error from FIG. 16 for allframes in a pullback.

FIG. 19 depicts the corresponding stent detections from for all framesin a pullback.

FIG. 20 depicts the resulting stent detections using regional covarianceanalysis.

FIG. 21 is a system diagram according to certain embodiments.

DESCRIPTION

This invention generally relates to automatically detecting stents inintraluminal medical imaging. Medical imaging is a general technologyclass in which sectional and multidimensional anatomic images areconstructed from acquired data. The data can be collected from a varietyof signal acquisition systems including, but not limited to, magneticresonance imaging (MRI), radiography methods including fluoroscopy,x-ray tomography, computed axial tomography and computed tomography,nuclear medicine techniques such as scintigraphy, positron emissiontomography and single photon emission computed tomography, photoacoustic imaging ultrasound devices and methods including, but notlimited to, intravascular ultrasound spectroscopy (IVUS), ultrasoundmodulated optical tomography, ultrasound transmission tomography, othertomographic techniques such as electrical capacitance, magneticinduction, functional MRI, optical projection and thermo-acousticimaging, combinations thereof and combinations with other medicaltechniques that produce one-, two- and three-dimensional images.Although the exemplifications described herein are drawn to theinvention as applied to OCT, at least all of these techniques arecontemplated for use with the systems and methods of the presentinvention.

Systems and methods of the invention have application in intravascularimaging methodologies such as intravascular ultrasound (IVUS) andoptical coherence tomography (OCT) among others that produce athree-dimensional image of a lumen. A segment of a lumen 101 is shown inFIG. 1 having a feature 113 of interest. FIG. 2 shows a cross-section oflumen 101 through feature 113. In certain embodiments, intravascularimaging involves positioning an imaging device near feature 113 andcollecting data representing a three-dimensional image.

OCT is a medical imaging methodology using a specially designed catheterwith a miniaturized near infrared light-emitting probe attached to thedistal end of the catheter. As an optical signal acquisition andprocessing method, it captures micrometer-resolution, three-dimensionalimages from within optical scattering media (e.g., biological tissue).Commercially available OCT systems are employed in diverse applications,including art conservation and diagnostic medicine, notably inophthalmology where it can be used to obtain detailed images from withinthe retina. The detailed images of the retina allow one to identifyseveral eye diseases and eye trauma. Recently it has also begun to beused in interventional cardiology to help diagnose coronary arterydisease. OCT allows the application of interferometric technology to seefrom inside, for example, blood vessels, visualizing the endothelium(inner wall) of blood vessels in living individuals.

Other applications of OCT and other signal processing imaging systemsfor biomedical imaging include use in: dermatology in order to imagesubsurface structural and blood flow formation; dentistry in order toimage the structure of teeth and gum line to identify and trackde-mineralization and re-mineralization, tarter, caries, and periodontaldisease; gastroenterology in order to image the gastrointestinal tractto detect polyps and inflammation, such as that caused by Crohn'sdisease and ulcerative colitis; cancer diagnostics in order todiscriminate between malignant and normal tissue.

Generally, an OCT system comprises three components which are 1) animaging catheter 2) OCT imaging hardware, 3) host application software.When utilized, the components are capable of obtaining OCT data,processing OCT data, and transmitting captured data to a host system.OCT systems and methods are generally described in Milner et al., U.S.Patent Application Publication No. 2011/0152771, Condit et al., U.S.Patent Application Publication No. 2010/0220334, Castella et al., U.S.Patent Application Publication No. 2009/0043191, Milner et al., U.S.Patent Application Publication No. 2008/0291463, and Kemp, N., U.S.Patent Application Publication No. 2008/0180683, the content of each ofwhich is incorporated by reference in its entirety. In certainembodiments, systems and methods of the invention include processinghardware configured to interact with more than one different threedimensional imaging system so that the tissue imaging devices andmethods described here in can be alternatively used with OCT, IVUS, orother hardware.

Various lumen of biological structures may be imaged with aforementionedimaging technologies in addition to blood vessels, including, but notlimited, to vasculature of the lymphatic and nervous systems, variousstructures of the gastrointestinal tract including lumen of the smallintestine, large intestine, stomach, esophagus, colon, pancreatic duct,bile duct, hepatic duct, lumen of the reproductive tract including thevas deferens, vagina, uterus and fallopian tubes, structures of theurinary tract including urinary collecting ducts, renal tubules, ureter,and bladder, and structures of the head and neck and pulmonary systemincluding sinuses, parotid, trachea, bronchi, and lungs.

The arteries of the heart are particularly useful to examine withimaging devices such as OCT. OCT imaging of the coronary arteries candetermine the amount of plaque built up at any particular point in thecoronary artery. The accumulation of plaque within the artery wall overdecades is the setup for vulnerable plaque which, in turn, leads toheart attack and stenosis (narrowing) of the artery. OCT is useful indetermining both plaque volume within the wall of the artery and/or thedegree of stenosis of the artery lumen. It can be especially useful insituations in which angiographic imaging is considered unreliable, suchas for the lumen of ostial lesions or where angiographic images do notvisualize lumen segments adequately. Example regions include those withmultiple overlapping arterial segments. It is also used to assess theeffects of treatments of stenosis such as with hydraulic angioplastyexpansion of the artery, with or without stents, and the results ofmedical therapy over time. In an exemplary embodiment, the inventionprovides a system for capturing a three dimensional image by OCT.

In OCT, a light source delivers a beam of light to an imaging device toimage target tissue. Light sources can be broad spectrum light sources,pulsating light sources, continuous wave light sources, and includesuperluminescent diodes, ultrashort pulsed lasers and supercontinuum.Within the light source is an optical amplifier and a tunable filterthat allows a user to select a wavelength of light to be amplified.Wavelengths commonly used in medical applications include near-infraredlight, for example between about 800 nm and about 1700 nm.

Methods of the invention apply to image data obtained from obtained fromany OCT system, including OCT systems that operate in either the timedomain or frequency (high definition) domain. Basic differences betweentime-domain OCT and frequency-domain OCT is that in time-domain OCT, thescanning mechanism is a movable mirror, which is scanned as a functionof time during the image acquisition. However, in the frequency-domainOCT, there are no moving parts and the image is scanned as a function offrequency or wavelength.

In time-domain OCT systems an interference spectrum is obtained bymoving the scanning mechanism, such as a reference mirror,longitudinally to change the reference path and match multiple opticalpaths due to reflections within the sample. The signal giving thereflectivity is sampled over time, and light traveling at a specificdistance creates interference in the detector. Moving the scanningmechanism laterally (or rotationally) across the sample producestwo-dimensional and three-dimensional images.

In frequency domain OCT, a light source capable of emitting a range ofoptical frequencies excites an interferometer, the interferometercombines the light returned from a sample with a reference beam of lightfrom the same source, and the intensity of the combined light isrecorded as a function of optical frequency to form an interferencespectrum. A Fourier transform of the interference spectrum provides thereflectance distribution along the depth within the sample.

Several methods of frequency domain OCT are described in the literature.In spectral-domain OCT (SD-OCT), also sometimes called “Spectral Radar”(Optics letters, Vol. 21, No. 14 (1996) 1087-1089), a grating or prismor other means is used to disperse the output of the interferometer intoits optical frequency components. The intensities of these separatedcomponents are measured using an array of optical detectors, eachdetector receiving an optical frequency or a fractional range of opticalfrequencies. The set of measurements from these optical detectors formsan interference spectrum (Smith, L. M. and C. C. Dobson, Applied Optics28: 3339-3342), wherein the distance to a scatterer is determined by thewavelength dependent fringe spacing within the power spectrum. SD-OCThas enabled the determination of distance and scattering intensity ofmultiple scatters lying along the illumination axis by analyzing asingle the exposure of an array of optical detectors so that no scanningin depth is necessary. Typically the light source emits a broad range ofoptical frequencies simultaneously. Alternatively, in swept-source OCT,the interference spectrum is recorded by using a source with adjustableoptical frequency, with the optical frequency of the source sweptthrough a range of optical frequencies, and recording the interferedlight intensity as a function of time during the sweep. An example ofswept-source OCT is described in U.S. Pat. No. 5,321,501.

Generally, time domain systems and frequency domain systems can furthervary in type based upon the optical layout of the systems: common beampath systems and differential beam path systems. A common beam pathsystem sends all produced light through a single optical fiber togenerate a reference signal and a sample signal whereas a differentialbeam path system splits the produced light such that a portion of thelight is directed to the sample and the other portion is directed to areference surface. Common beam path systems are described in U.S. Pat.No. 7,999,938; U.S. Pat. No. 7,995,210; and U.S. Pat. No. 7,787,127 anddifferential beam path systems are described in U.S. Pat. No. 7,783,337;U.S. Pat. No. 6,134,003; and U.S. Pat. No. 6,421,164, the contents ofeach of which are incorporated by reference herein in its entirety.

In certain embodiments, the invention provides a differential beam pathOCT system with intravascular imaging capability as illustrated in FIG.3. For intravascular imaging, a light beam is delivered to the vessellumen via a fiber-optic based imaging catheter 826. The imaging catheteris connected through hardware to software on a host workstation. Thehardware includes an imagining engine 859 and a handheld patientinterface module (PIM) 839 that includes user controls. The proximal endof the imaging catheter is connected to PIM 839, which is connected toan imaging engine as shown in FIG. 3.

As shown in FIG. 4, the imaging engine 859 (e.g., a bedside unit) housesa power supply 849, light source 827, interferometer 831, and variabledelay line 835 as well as a data acquisition (DAQ) board 855 and opticalcontroller board (OCB) 851. A PIM cable 841 connects the imagine engine859 to the PIM 839 and an engine cable 845 connects the imaging engine859 to the host workstation.

FIG. 5 shows light path in a differential beam path system according toan exemplary embodiment of the invention. Light for image captureoriginates within the light source 827. This light is split between anOCT interferometer 905 and an auxiliary, or “clock”, interferometer 911.Light directed to the OCT interferometer is further split by splitter917 and recombined by splitter 919 with an asymmetric split ratio. Themajority of the light is guided into the sample path 913 and theremainder into a reference path 915. The sample path includes opticalfibers running through the PIM 839 and the imaging catheter 826 andterminating at the distal end of the imaging catheter where the image iscaptured.

Typical intravascular OCT involves introducing the imaging catheter intoa patient's target vessel using standard interventional techniques andtools such as a guide wire, guide catheter, and angiography system. Theimaging catheter may be integrated with IVUS by an OCT-IVUS system forconcurrent imaging, as described in, for example, Castella et al. U.S.Patent Application Publication No. 2009/0043191 and Dick et al. U.S.Patent Application Publication No. 2009/0018393, both incorporated byreference in their entirety herein.

Rotation of the imaging catheter is driven by spin motor 861 whiletranslation is driven by pullback motor 865, shown in FIG. 6. Thisresults in a motion for image capture described by FIG. 7. Blood in thevessel is temporarily flushed with a clear solution for imaging. Whenoperation is triggered from the PIM or control console, the imaging coreof the catheter rotates while collecting image data. Using lightprovided by the imaging engine, the inner core sends light into thetissue in an array of A-scan lines as illustrated in FIG. 8 and detectsreflected light.

FIG. 9 shows the positioning of A-scans within a vessel. Each placewhere one of A-scans A11, A12, . . . , AN intersects a surface of afeature within vessel 101 (e.g., a vessel wall) coherent light isreflected and detected. Catheter 826 translates along axis 117 beingpushed or pulled by pullback motor 865.

The reflected, detected light is transmitted along sample path 913 to berecombined with the light from reference path 915 at splitter 919 (FIG.5). A variable delay line (VDL) 925 on the reference path uses anadjustable fiber coil to match the length of reference path 915 to thelength of sample path 913. The reference path length is adjusted by astepper motor translating a mirror on a translation stage under thecontrol of firmware or software. The free-space optical beam on theinside of the VDL 925 experiences more delay as the mirror moves awayfrom the fixed input/output fiber.

The combined light from splitter 919 is split into orthogonalpolarization states, resulting in RF-band polarization-diverse temporalinterference fringe signals. The interference fringe signals areconverted to photocurrents using PIN photodiodes 929 a, 929 b, . . . onthe OCB 851 as shown in FIG. 5. The interfering, polarization splitting,and detection steps are done by a polarization diversity module (PDM) onthe OCB. Signal from the OCB is sent to the DAQ 855, shown in FIG. 4.The DAQ includes a digital signal processing (DSP) microprocessor and afield programmable gate array (FPGA) to digitize signals and communicatewith the host workstation and the PIM. The FPGA converts raw opticalinterference signals into meaningful OCT images. The DAQ also compressesdata as necessary to reduce image transfer bandwidth to 1 Gbps (e.g.,compressing frames with a glossy compression JPEG encoder).

Data is collected from A-scans A11, A12, . . . , AN and stored in atangible, non-transitory memory. Typically, rotational systems consistof an imaging core which rotates and pulls back (or pushes forward)while recording an image video loop. This motion results in a threedimensional dataset of two dimensional image frames, where each frameprovides a 360° slice of the vessel at different longitudinal locations.

A set of A-scans generally corresponding to one rotation of catheter 826around axis 117 collectively define a B-scan. FIG. 10 illustrates a setof A-scans A11, A12, . . . , A18 used to compose a B-scan according tocertain embodiments of the invention. These A-scan lines are shown aswould be seen looking down axis 117 (i.e., longitudinal distance betweenthen is not shown). While eight A-scan lines are illustrated in FIG. 10,typical OCT applications can include between 300 and 1,000 A-scan linesto create a B-scan (e.g., about 660). Reflections detected along eachA-scan line are associated with features within the imaged tissue.Reflected light from each A-scan is combined with corresponding lightthat was split and sent through reference path 915 and VDL 925 andinterference between these two light paths as they are recombinedindicates features in the tissue.

The data of all the A-scan lines together represent a three-dimensionalimage of the tissue. The data of the A-scan lines generally referred toas a B-scan can be used to create an image of a cross section of thetissue, sometimes referred to as a tomographic view. For example, FIG.11 shows the set of A-scans shown in FIG. 10 within a cross section of avessel.

The set of A-scans obtained by rotational imaging modality can becombined to form a B-scan. FIG. 12 is an example of an OCT polarcoordinate B-Scan with 660 A-scans. To create a final tomographic viewof the vessel, the B-scan is scan converted to a Cartesian coordinatesystem. FIG. 13 displays the scan-converted image of the B-scan in FIG.12.

Systems and methods of the invention include image-processing techniquesthat provide automatic detection of objects, such as stents, withinintraluminal images. Typically, the OCT intraluminal image is anintravascular image taken within a lumen of a blood vessel, but thedetection methods described herein can be used to detect objects withinother biological lumens, such as the intestine. Although the followingdescription is directed towards detecting objects in OCT images, oneskilled in the art would readily recognize that methods and systems ofintention can be utilized to detect objects in any intraluminal imagesobtained from any other imaging technique, such as intravascularultrasound imaging (IVUS) and combined OCT-IVUS.

Embodiments of the invention provide for algorithms to detect a stentslocation within the polar coordinate system using features withinone-dimensional images, such as A-scan, two-dimensional images, such asa B-scan, and/or three-dimensional images. Once the polar coordinates ofthe object are detected, the polar coordinates can be converted to theCartesian coordinates and displayed as a tomographic image. Thus, athree-dimensional profile of the stent can be detected and displayed toa user. In addition, with the polar coordinates of the stentautomatically detected, the strent stut apposition or coverage relativeto the lumen border can easily be computed. Additionally, thesealgorithms can be applied to pre-scan converted data and to scanconverted data.

Because the algorithms disclosed herein can be applied to every frametaken during an OCT imaging run, the location of the object can bedetected in one or more frames can be computed and provided to the useron a graphic display.

The 1-D, 2-D or 3-D images include data, such as pixel data, whichincludes pixel locations, pixel intensity, color intensities, whichincludes the RGB color channel for the pixels, and/or volumetric data,which includes the x, y, z coordinates. The data obtained from the imageare considered features within the image that can be used to classify ordetect the object. Images can be associated with other data featuressuch as amplitude, phase, frequency, polarity, velocity, weight,density, transparency, reflectance, hardness, and temperature.

FIG. 14 exemplifies the steps employed in an embodiment for detectingstents using an adaptation of principal component analysis, a knownsignal processing approach. Exemplary principal components analysistechniques can be found in M. Turk and A. Pentland “Eigenfaces forRecognition” and Pentland et al. “View-based and modular eigenspaces forface recognition” in Proc. IEEE Conf. Comput Vision. @ Pattern Recogn.These techniques have been adapted to of detection of object inintraluminal images. The first step 30 includes generating a trainingset of pre-defined intraluminal images for an object. The second step 32involves computing the principal components for the object to create anobject space. The third step 34 involves projecting an inputintraluminal image onto the object space. The fourth step 36 involvesdetecting the object within the intraluminal image. Methods of theinvention are not limited to stent detection, but can be used to detectany object within an intraluminal image, such as guidewire, lumenborder, and tissue.

The first step 30 is to generate a training set of pre-definedintraluminal images known to contain an object so that the images can beused to train the processor to identify the object within images orregions of images not known to contain the object. Images known tocontain the object include images in which the object was manuallylocated and detected. The images known to contain the object can beobtained online or compiled off-line. In certain aspects, the trainingset of pre-defined intraluminal images can be pre-processed by, forexample filtering the images prior to generating a training set.

In certain aspects, the images for the training set are all the samesize or interpolated to the same size. Data, such as pixel intensity,can be arranged into a matrix for principal component analysis. In oneaspect, data from each image in the training set can be taken andstacked into a matrix, wherein each column in the matrix represents adifferent image and each row within a column of the matrix representsthe same pixel location in each training image. The rows can be throughof as features and each column is a sample of that feature.

It should be noted that in all training sets generated for use in theembodiments described herein are not limited to a fixed amount ofpre-defined images. Rather, the training sets can be made to have anadaptive training process. For example, by continually updating trainingsets with input intraluminal images that have been positively identifiedfor a specific object. As the training set database grows, so does theaccuracy of the detection.

Once a matrix for the training set of pre-defined matrix is compiled,the principal components for the training set matrix are computed tocreate an object space, as in the second step 32. The principalcomponents can be computed by directly computing the eigenvectors of acovariance matrix computed from the training data set matrix or byutilizing Singular Value Decomposition (SVD) of the training set datamatrix, which is described in detail in, for example, Abdi, H, &Williams, L. J. (2010). “Principal component analysis.” WileyInterdisciplinary Reviews: Computational Statistics, 2: 433-459. Bycalculating the principal components, one can determine which vectorsout of the set of pre-defined images best account for the distributionof object images within the entire object space. Therefore, only the topn eigenvectors are kept in order to create a basis which accounts formost of the variance within the image set. These vectors define thesubspace of object images, and constitute the object space. Theprincipal components are stored within a memory and utilized later on todetect an object within input intraluminal images.

In order to utilize the principal components to detect object in unknowninput images, a threshold error can be computed. In one aspect, thethreshold value is computed by determining the amount of error betweenone or more of pre-defined images known to contain the object and theobject space. This threshold error can be obtained using the samepre-defined images that were used to create the object space or anotherimage known to create the same object. In order to determine error, apre-defined image can be projected onto the object space in order todetermine the distance between points in the pre-defined image incomparison to the object space. In certain aspects, the error is theEuclidean distance between the training set image and the object space.This error computation can be repeated for each pre-defined image in thetraining set, a portion of pre-defined images in the training set, orfor multiple pre-defined images outside of the training set.

Using the computed errors, one can calculate a threshold error valuethat can be used to determine if an unknown image contains the object.For example, unknown images that are projected against the object spacethat have an error greater than the threshold error will not bedetermined to contain the object and unknown images with an errorsmaller than the threshold error will return a positive detection forthe object. The threshold error can be the maximum error, minimum error,or an average computation of the error, such as the quadratic mean,arithmetic mean, mode, medium, or any other statistical average known inthe art.

The third step 34 involves projecting an input intraluminal image ontothe object space. The input intraluminal image can be an image takenduring an OCT procedure in real-time or a previously taken image, forexample, an image that was stored or uploaded onto the computing system.The error between the input intraluminal image and the object space iscomputed in a similar manner the error was computed for each pre-definedimage to determine a threshold error. In some embodiments, the error isthe Euclidean distance between the input image and the object space.After the error is computed, the error of the input intraluminal imagecan be used to detect the object, as in the fourth step 36. For example,if the error is below a threshold value, the object is positivelydetected in the input intraluminal image. If the error is above thethreshold image, then the object is negatively detected within inputintraluminal image.

In certain aspects, an object space is created for two or more objectsin order to compare the input intraluminal image to two or more objectspaces. Step 30 and step 32 are repeated for at least one other object.In one embodiment, a training set is generated of pre-definedintraluminal images known to contain stents and a training set isgenerated of pre-defined intraluminal images known to contain tissue,for example, imaging of a blood vessel without stents. The principalcomponents are generated for both training sets to compute a tissuespace and a stent space. For step 36, an input intraluminal image can beprojected onto both the tissue space and the stent space, and a tissueerror and a stent error can be calculated by comparing the inputintraluminal image to both spaces. The set of principal components,tissue or stent, which most accurately represents the original image, isselected as the class matching the input intraluminal image, and thecorresponding object is positively detected. For example, if the errorbetween the stent and the input intraluminal image is less than theerror between the tissue and the input intraluminal image, the stent ispositively detected within the input intraluminal image.

In addition, a threshold error value can also be computed for each ofthe plurality of object spaces. A comparison between the inputintraluminal image and each object space's threshold value can determinewhether or not the object is present in the input intraluminal image. Ifthe error is significantly high for both classes, this can indicate thatthe input intraluminal image does not match any of the data that wasused in the training set. Comparing threshold error reduces the risk ofmisclassification when comparing simply comparing the magnitudes of theerror. If the input intraluminal image does not match any of thetraining sets, an indicator can appear on an OCT graphical display toindicate to the user that manual detection may be required with respectto the unclassified input intraluminal image.

In a specific embodiment, method of FIG. 14 is adapted to train theclassifier to detect tissue, stents, and guidewires. Guidewires areoften misclassified as a stent strut because its features appear stentlike in the intraluminal images. This prevents the likelihood that apositive detection for a stent is actually a guidewire.

In some embodiments, the input intraluminal image may be defined usingthe lumen border. In order to improve performance, the detected lumenborder can be used to identify search regions for the object, such as astent strut, within the image. In this aspect, the training sets ofpre-defined images generated for an object will also be defined by thelumen border. For example, if a lumen border is detected within a regionaround +30 pixels, −200 pixels within an A-line, a training set can beformed using only the lumen border region of pre-defined images and anobject space for that region can be generated. The same region of theA-line intraluminal image can be projected onto the object space todetect the object in that region. Detection occurs using the same errormethods as previously described. The lumen border can be automaticallyor semi-automatically detected in an image using any method known in theart, such as the techniques disclosed in U.S. Pat. No. 7,978,916, S.Tanimoto, G. Rodriguez-Granillo, P. Barlis, S. de Winter, N. Bruining,R. Hamers, M. Knappen, S. Verheye, P. W. Serruys, and E. Regar, “A novelapproach for quantitative analysis of intracoronary opticalcoherencetomography: High inter-observer agreement withcomputer-assisted contour detection,” Cathet. Cardiovasc. Intervent. 72,228-235 (2008); K. Sihan, C. Botka, F. Post, S. de Winter, E. Regar, R.Hamers, and N. Bruining, “A novel approach to quantitative analysis ofintraluminal optical coherence tomography imaging,” Comput. Cardiol.1089-1092 (2008); J. Canny, “A computational approach to edgedetection,” IEEE Trans. Pattern Anal. Mach. Intell. 8, 679-698 (1986).

Additionally, methods of the invention provide for a post-processingstep to, for example, detect the location of the stent within the image,for example, the stent depth. Any method known in the art can be used tolocate the depth position of the stent, such as peak detection withinsome maximum distance of the detected lumen border can be used toidentify the final location of the stent. Post-processing can be used toverify the detection of the stent within the image. For example, methodsof the invention can also be combined with other algorithms or methodsused to detect guidewires or other false stent detections. In oneaspect, after detection of stents using the methods of the invention, aguidewire detection/tracking algorithm can be applied to the image toremove the false stent detections. Post-processing can also be used tovisually illustrate the resulting stent detections within theintravascular image on a graphical user interface. For example, detectedportions of the stent can be highlighted with a bolded line or circledwithin the image.

The following description and figures illustrate stent detectionfollowing the block diagram in FIG. 14. FIG. 15 depicts an example of anOCT B-scan highlighting both detected stent struts and the automaticallydetected lumen border. A training set for stents and tissue weregenerated defined by the lumen border and a tissue space and stent spacewere computed. The A-line input intraluminal image around the border wasprojected onto a stent space and a tissue space. The error between theinput intraluminal image and both the tissue space and stent space werecomputed, and plotted in FIG. 16. As shown in FIG. 16, the stent erroris smaller at the location of the stents and the tissue error is higherat the locations of the stents. The difference between the stent errorand the tissue error is also plotted in FIG. 16. Locations along thelumen border where the stent error is lower than the tissue error areclassified as stents, and thus a stent is positively detected within theimage. FIG. 17 displays the corresponding scan-converted image of theB-scan shown in FIG. 15. Post-processing was utilized to highlight thestent detections within the scan-converted image. The difference betweenthe stent and tissue error for all frames in a pullback is plotted in a2D splayed map in FIG. 18. The corresponding stent detections for allframes in this pull-back are provided in FIG. 19.

In another embodiment, objects are detected within an intraluminal imageusing region covariance descriptors to detect objects in images or inregions of images. This approach can be adapted to both 1D, 2D, and 3Dintraluminal images. Similar to the detection method outlined in FIG.13, this algorithm requires generating a training step of pre-definedintraluminal images and determining a compute for the training set thatis compared to an input intraluminal image for detection. Regionalcovariance imaging techniques are known in the art and are described in,for example, Tuzel et al. “Region Covariance: A Fast Descriptor forDetection and Classification,” European Conference on Computer Vision(ECCV), May 2006; Forstner and Moonen, “A metric for covariancematrices,” Technical Report, Dept. of Geodesy and Geoinformatics,Stuttgart University (1999).

To detect stents or objects within intraluminal images using regionalcovariance, the first step is to generate a training set of pre-definedintraluminal images known to contain an object, for example, stent,tissue, or guidewire. A feature matrix can then be generated for thetraining set using a number of features within each pre-definedintraluminal image of the training set, e.g., the x and y coordinates ofthe pixel location, intensity of each pixel, and the first and secondorder derivatives of the image in the x and y direction. These featuresare computes for each pre-defined image within the training set. Apre-defined image of the training set can be any size image of m×n, andin one aspect m and n correspond to a dimension that is slightly largerthan the width of a stent and the depth of tissue in an intraluminalimage, and all images of the training set should be the same size.Although it is possible to perform regional covariance analysis on theentire image, use of m×n regions allows for targeted search of stentsand other objects located on the lumen border. For example, the m×nimage region can be created around the lumen border detected within aninput intraluminal image, using any method of detecting the lumen borderknown in the art and discussed above.

Each pixel of a pre-defined intraluminal image is converted in to afeature matrix, using the equation listed below.

$\begin{matrix}{{Equation}\mspace{14mu} 1\text{:}\mspace{14mu}{Feature}\mspace{14mu}{Matrix}\mspace{14mu}{for}\mspace{14mu}{Region}\mspace{14mu}{Covariance}\mspace{14mu}{{Tracking}\mspace{14mu}\lbrack 1\rbrack}} & \; \\{{F\left( {x,y} \right)} = \left\lbrack {❘{x\mspace{14mu} y\mspace{14mu}{I\left( {x,y} \right)}\mspace{14mu}{\frac{\partial{I\left( {x,y} \right)}}{\partial x}}\mspace{14mu}{\frac{\partial{I\left( {x,y} \right)}}{\partial y}}\mspace{14mu}{\frac{\partial^{2}{I\left( {x,y} \right)}}{\partial x^{2}}}\mspace{14mu}{\frac{\partial^{2}{I\left( {x,y} \right)}}{\partial y^{2}}}}} \right\rbrack} & {{Equation}\mspace{14mu} 1}\end{matrix}$In the above equation, x and y are indices and I is the intensity. Forpurposes of stent detection, the feature equation can be adapted to havemore or less features or contain additional feature data, for example,the addition of RGB color values. Each input intraluminal image withinthe training set will have the same (x, y) pixel locations, and althoughnot distinguishing, these coordinates are useful to correlate otherfeatures that vary from image to image. Once the feature matrix iscomputed, a covariance matrix for the set of features for each image canbe computed using the following equation, where z represents thefeatures, u is the mean of the feature samples, and T is a transposeoperator.

$\begin{matrix}{C_{R} = {\frac{1}{n - 1}{\sum\limits_{k = 1}^{n}\;{\left( {z_{k} - \mu} \right)\left( {z_{k} - \mu} \right)^{T}}}}} & {{Equation}\mspace{14mu} 2}\end{matrix}$

The above process is repeated for each pre-defined intraluminal imagewithin the training set, and the covariance matrices are saved in thememory for later use during detection of objects of unknown inputintraluminal images. The covariance matrices represent subspaces of theobject.

In order to detect an object in the input intraluminal image, the inputintraluminal image is broken down into the same m×n regions as thepre-defined images of the training set to identify, for example, stentlocations. The covariance matrix of the input intraluminal image for theregion is computed and compared to each covariance matrix of thetraining set. The comparison of the covariance matrix involvesperforming a distance calculation, or error calculation, between featurepoints within the covariance matrices. Any method known in the art forcalculating the distance between covariance matrices can be used oradapted to calculate the distance between the unknown input intraluminalimage covariance matrix and the covariance matrices of the pre-definedtraining. See for example, J. N. L. Brümmer and L. R. Strydom, “Aneuclidean distance measure between covariance matrices of speechcepstrafor text-independent speaker recognition,” in Proc. 1997 South AfricanSymp. Communications and Signal Processing, 1997, pp. 167-172; W.Förstner and B. Moonen, A Metric for Covariance Matrices Dept. Geodesyand Geoinformatics, Stuttgart Univ., Stuttgart, Germany, 1999; Ö Tüzel,F. Porikli, and P. Meer, “Region covariance: A fast descriptor and fordetection and classification,” in Proc. Image and Vision Computing,Auckland, New Zealand, 2004.

A threshold error can be determined for the training set and used todetermine whether the distance between the input intraluminal image andtraining set images are indicative of a positive detection of theobject. Any method can be used to create a threshold distance. Forexample, the threshold distance is obtained by calculating thecovariance distance between the training set images and selecting themaximum distance as a threshold distance, or calculating an averagevalue as a threshold distance.

Like previous embodiments, the regional covariance approach can also beused to detect one or more objects within an input intravascular imageby generating covariance matrices for more than one object. For example,a training set of pre-defined images can be generated for tissue andstents, features matrices can be computed for each pre-defined imagewithin a training set, and a covariance matrix can be calculated fromeach feature matrix. A covariance matrix calculated for an inputintraluminal image is then compared to the stent and tissue covariancematrices. The training set that minimizes the distance from inputintraluminal image indicates a positive detection of the objectcorresponding to the training set within the input intraluminal image.In addition, a threshold error can be computed for each object, and usedto determine if the either object is present in the intravascular image.

FIG. 20 displays detected stent struts within A-scan-converted imageusing the regional covariance approach. The bolded lines indicate thestent detections. Like the previously discussed embodiments,post-processing can be applied to identify the location of the stent indepth and remove false detections.

In addition, other algorithm image processing techniques known in theart that utilize subspaces for object recognition within images canadapted to detect stents and other objects in intraluminal images. For aconcise overview of various object recognition techniques, see Bain andTao, Chapter 3: “Face Subspace Learning”, Handbook of Face Recognition,2011. For example, Fisher's linear discriminate analysis (FLDA) can beused to detect stents. Linear discriminant analysis is primarily used toreduce the number of features, such as pixel values, to a moremanageable number before classification or detection as compared tousing principal component analysis. Each of the new dimensions is alinear combination of pixel values, which form a template. The linearcombinations obtained using Fisher's linear discriminant is called alinear classifier and can be used in comparison to input intraluminalimages to detect stents.

In certain aspects, FDLA can be combined with other algorithmictechniques to improve the accuracy of object detection using thetechnique. For example, FDLA can be combined with general mean criterionand max-min distance analysis (MMDA), discriminatory locality alignmentanalysis (DLA), and manifold elastic net (MNE).

Another detection method that can be used or adapted to detect stents orobjects in intraluminal images includes using statistical model-basedimage recognition algorithms. See, for example, Felzenszwalb andHuttenlocher, “Pictorial Structures for Object Recognition,” Volume 61,Number 1, 55-79, DOI: 10.1023/B:VISI.0000042934.15159.49; A. A. Amini,T. E. Weymouth, and R. C. Jain. “Using dynamic programming for solvingvariational problems in vision,” IEEE Transactions on Pattern Analysisand Machine Intelligence, 12(9):8551{867, September 1990; M. A. Fischlerand R. A. Elschlager, “The representation and matching of pictorialstructures,” IEEE Transactions on Computer, 22(1):67-92, January 1973.

With respect to the methods of detecting objects within intraluminalimages discussed herein, various computer or processor-based systems aresuitable for compiling data from intraluminal images, interfacing withan OCT probe to obtain input intraluminal images, applying the disclosedalgorithms to detect objects, and displaying the detected objects to auser of the OCT system. The systems and methods of use described hereinmay take the form of an entirely hardware embodiment, an entirelysoftware embodiment, or an embodiment combining software and hardwareaspects. The systems and methods of use described herein can beperformed using any type of computing device, such as a computer, thatincludes a processor or any combination of computing devices where eachdevice performs at least part of the process or method.

In some embodiments, a device of the invention includes an OCT imagingsystem and obtains a three-dimensional data set through the operation ofOCT imaging hardware. In some embodiments, a device of the invention isa computer device such as a laptop, desktop, or tablet computer, andobtains a three-dimensional data set by retrieving it from a tangiblestorage medium, such as a disk drive on a server using a network or asan email attachment.

Methods of the invention can be performed using software, hardware,firmware, hardwiring, or combinations of any of these. Featuresimplementing functions can also be physically located at variouspositions, including being distributed such that portions of functionsare implemented at different physical locations (e.g., imaging apparatusin one room and host workstation in another, or in separate buildings,for example, with wireless or wired connections).

In some embodiments, a user interacts with a visual interface to viewimages from the imaging system. Input from a user (e.g., parameters or aselection) are received by a processor in an electronic device. Theselection can be rendered into a visible display. An exemplary systemincluding an electronic device is illustrated in FIG. 21. As shown inFIG. 21, imaging engine 859 communicates with host workstation 433 aswell as optionally server 413 over network 409. In some embodiments, anoperator uses computer 449 or terminal 467 to control system 400 or toreceive images. An image may be displayed using an I/O 454, 437, or 471,which may include a monitor. Any I/O may include a keyboard, mouse ortouchscreen to communicate with any of processor 421, 459, 441, or 475,for example, to cause data to be stored in any tangible, nontransitorymemory 463, 445, 479, or 429. Server 413 generally includes an interfacemodule 425 to effectuate communication over network 409 or write data todata file 417.

Processors suitable for the execution of computer program include, byway of example, both general and special purpose microprocessors, andany one or more processor of any kind of digital computer. Generally, aprocessor will receive instructions and data from a read-only memory ora random access memory or both. The essential elements of computer are aprocessor for executing instructions and one or more memory devices forstoring instructions and data. Generally, a computer will also include,or be operatively coupled to receive data from or transfer data to, orboth, one or more mass storage devices for storing data, e.g., magnetic,magneto-optical disks, or optical disks. Information carriers suitablefor embodying computer program instructions and data include all formsof non-volatile memory, including by way of example semiconductor memorydevices, (e.g., EPROM, EEPROM, solid state drive (SSD), and flash memorydevices); magnetic disks, (e.g., internal hard disks or removabledisks); magneto-optical disks; and optical disks (e.g., CD and DVDdisks). The processor and the memory can be supplemented by, orincorporated in, special purpose logic circuitry.

To provide for interaction with a user, the subject matter describedherein can be implemented on a computer having an I/O device, e.g., aCRT, LCD, LED, or projection device for displaying information to theuser and an input or output device such as a keyboard and a pointingdevice, (e.g., a mouse or a trackball), by which the user can provideinput to the computer. Other kinds of devices can be used to provide forinteraction with a user as well. For example, feedback provided to theuser can be any form of sensory feedback, (e.g., visual feedback,auditory feedback, or tactile feedback), and input from the user can bereceived in any form, including acoustic, speech, or tactile input.

The subject matter described herein can be implemented in a computingsystem that includes a back-end component (e.g., a data server 413), amiddleware component (e.g., an application server), or a front-endcomponent (e.g., a client computer 449 having a graphical user interface454 or a web browser through which a user can interact with animplementation of the subject matter described herein), or anycombination of such back-end, middleware, and front-end components. Thecomponents of the system can be interconnected through network 409 byany form or medium of digital data communication, e.g., a communicationnetwork. Examples of communication networks include cell network (e.g.,3G or 4G), a local area network (LAN), and a wide area network (WAN),e.g., the Internet.

The subject matter described herein can be implemented as one or morecomputer program products, such as one or more computer programstangibly embodied in an information carrier (e.g., in a non-transitorycomputer-readable medium) for execution by, or to control the operationof, data processing apparatus (e.g., a programmable processor, acomputer, or multiple computers). A computer program (also known as aprogram, software, software application, app, macro, or code) can bewritten in any form of programming language, including compiled orinterpreted languages (e.g., C, C++, Perl), and it can be deployed inany form, including as a stand-alone program or as a module, component,subroutine, or other unit suitable for use in a computing environment.Systems and methods of the invention can include instructions written inany suitable programming language known in the art, including, withoutlimitation, C, C++, Perl, Java, ActiveX, HTML5, Visual Basic, orJavaScript.

A computer program does not necessarily correspond to a file. A programcan be stored in a portion of file 417 that holds other programs ordata, in a single file dedicated to the program in question, or inmultiple coordinated files (e.g., files that store one or more modules,sub-programs, or portions of code). A computer program can be deployedto be executed on one computer or on multiple computers at one site ordistributed across multiple sites and interconnected by a communicationnetwork.

A file can be a digital file, for example, stored on a hard drive, SSD,CD, or other tangible, non-transitory medium. A file can be sent fromone device to another over network 409 (e.g., as packets being sent froma server to a client, for example, through a Network Interface Card,modem, wireless card, or similar).

Writing a file according to the invention involves transforming atangible, non-transitory computer-readable medium, for example, byadding, removing, or rearranging particles (e.g., with a net charge ordipole moment into patterns of magnetization by read/write heads), thepatterns then representing new collocations of information aboutobjective physical phenomena desired by, and useful to, the user. Insome embodiments, writing involves a physical transformation of materialin tangible, non-transitory computer readable media (e.g., with certainoptical properties so that optical read/write devices can then read thenew and useful collocation of information, e.g., burning a CD-ROM). Insome embodiments, writing a file includes transforming a physical flashmemory apparatus such as NAND flash memory device and storinginformation by transforming physical elements in an array of memorycells made from floating-gate transistors. Methods of writing a file arewell-known in the art and, for example, can be invoked manually orautomatically by a program or by a save command from software or a writecommand from a programming language.

Suitable computing devices typically include mass memory, at least onegraphical user interface, at least one display device, and typicallyinclude communication between devices. The mass memory illustrates atype of computer-readable media, namely computer storage media. Computerstorage media may include volatile, nonvolatile, removable, andnon-removable media implemented in any method or technology for storageof information, such as computer readable instructions, data structures,program modules, or other data. Examples of computer storage mediainclude RAM, ROM, EEPROM, flash memory, or other memory technology,CD-ROM, digital versatile disks (DVD) or other optical storage, magneticcassettes, magnetic tape, magnetic disk storage or other magneticstorage devices, Radiofrequency Identification tags or chips, or anyother medium which can be used to store the desired information andwhich can be accessed by a computing device.

It will be understood that each block of the FIG. 14, as well as anyportion of the systems and methods disclosed herein, can be implementedby computer program instructions. These program instructions may beprovided to a processor to produce a machine, such that theinstructions, which execute on the processor, create means forimplementing the actions specified in the FIG. 14 or described for thesystems and methods disclosed herein. The computer program instructionsmay be executed by a processor to cause a series of operational steps tobe performed by the processor to produce a computer implemented process.The computer program instructions may also cause at least some of theoperational steps to be performed in parallel. Moreover, some of thesteps may also be performed across more than one processor, such asmight arise in a multi-processor computer system. In addition, one ormore processes may also be performed concurrently with other processesor even in a different sequence than illustrated without departing fromthe scope or spirit of the invention.

INCORPORATION BY REFERENCE

References and citations to other documents, such as patents, patentapplications, patent publications, journals, books, papers, webcontents, have been made throughout this disclosure. All such documentsare hereby incorporated herein by reference in their entirety for allpurposes.

EQUIVALENTS

Various modifications of the invention and many further embodimentsthereof, in addition to those shown and described herein, will becomeapparent to those skilled in the art from the full contents of thisdocument, including references to the scientific and patent literaturecited herein. The subject matter herein contains important information,exemplification and guidance that can be adapted to the practice of thisinvention in its various embodiments and equivalents thereof.

What is claimed is:
 1. A computer-readable, non-transitory mediumstoring software code representing instructions that when executed by acomputing system cause the computing system to perform a method ofdetecting an object within an intraluminal image, the method comprisinggenerating a set of pre-defined intraluminal images known to display anobject; computing principal components for the set to create an objectspace for the object; projecting an input intraluminal image onto theobject space; and detecting the object in the input intraluminal image,wherein the object is selected from the group comprising a stent strutor a guidewire.
 2. The computer-readable, non-transitory medium of claim1, wherein the step of detecting further comprises calculating an errorbetween the input intraluminal image and the object space.
 3. Thecomputer-readable, non-transitory medium of claim 2, wherein a smallerror constitutes a positive detection of the object in the inputintraluminal image.
 4. The computer-readable, non-transitory medium ofclaim 1, wherein the pre-defined intraluminal images and inputintraluminal image are one-dimensional, two-dimensional, orthree-dimensional.
 5. The computer-readable, non-transitory medium ofclaim 1, further comprising post-processing the input intraluminalimage.
 6. The computer-readable, non-transitory medium of claim 5,wherein the step of post-processing comprises removing false objectdetections and highlighting the object detections.
 7. Thecomputer-readable, non-transitory medium of claim 1, further comprisingperforming the steps of generating, identifying, and projecting for atleast one other object; and detecting the at least one other object inthe input intraluminal image.
 8. The computer-readable, non-transitorymedium of claim 7, wherein the step of detecting the at least one otherobject further comprises calculating an error between the inputintraluminal image and the object space for the object and between theinput intraluminal image and the object space for the at least one otherobject.
 9. The computer-readable, non-transitory medium of claim 7,further wherein the smaller error constitutes a positive detection forthe corresponding object.
 10. The computer-readable, non-transitorymedium of claim 7, wherein the pre-defined intraluminal images and inputintraluminal image are one-dimensional, two-dimensional, orthree-dimensional.
 11. The computer-readable, non-transitory medium ofclaim 7, wherein the object is selected from the group consisting of atissue, a stent strut, or a guidewire.
 12. The computer-readable,non-transitory medium of claim 7, further comprising post-processing theinput intraluminal image.
 13. The computer-readable, non-transitorymedium of claim 12, wherein post-processing comprises removing falseobject detections and highlighting the object detections.
 14. A systemfor automatically detecting an object within an intraluminal image,comprising: a central processing unit (CPU); and a storage devicecoupled to the CPU and having stored there information for configuringthe CPU to: generate a set of pre-defined intraluminal images known todisplay a object; compute principal components for the set to create anobject space for the object; and project an input intraluminal dataimage onto the object space; detect the object in the input intraluminalimage, wherein the object is selected from the group comprising a stentstrut or a guidewire.
 15. The system of claim 14, wherein detecting theobject further comprises calculating an error between the inputintraluminal image and the object space.
 16. The system of claim 15,wherein a small error as compared to the object space constitutes apositive detection of the object in the input intraluminal image. 17.The system of claim 14, wherein the pre-defined intraluminal images andinput intraluminal image are one-dimensional, two-dimensional, orthree-dimensional.
 18. The system of claim 14, further comprisingpost-processing the input intraluminal image.
 19. The system of claim18, wherein post-processing comprises removing false object detectionsand highlighting the object detections.
 20. The system of claim 14,further comprising performing the steps of generating, identifying, andprojecting for at least one other object; and detecting the at least oneother object in the input intraluminal image.
 21. The system of claim20, wherein the detecting the at least one other object furthercomprises calculating an error between the input intraluminal image andthe object space for the object and between the input intraluminal imageand the object space for the at least one other object.
 22. The systemof claim 21, further wherein the smaller error constitutes a positivedetection for the corresponding object.
 23. The system of claim 20,wherein the pre-defined intraluminal images and input intraluminal imageare one-dimensional, two-dimensional, or three-dimensional.
 24. Thesystem of claim 20, wherein the object is selected from the groupconsisting of a lumen border, a stent strut, or a guidewire.
 25. Thesystem of claim 20, further comprising post-processing the inputintraluminal image.
 26. The system of claim 25, wherein post-processingcomprises removing false object detections and highlighting the objectdetections.
 27. A computer-readable, non-transitory medium storingsoftware code representing instructions that when executed by acomputing system cause the computing system to perform a method ofdetecting an object within a blood vessel, the method comprising:generating a training set of pre-defined intraluminal images known todisplay an object, each intraluminal image comprising a feature;computing a covariance for a feature within each intraluminal image ofthe training set; and detecting the object within an input intraluminalimage, the detecting step comprising: computing a covariance for afeature within the intraluminal input image; and comparing thecovariance of the input intraluminal image to the covariances of thetraining set to detect the object in the input image, wherein the objectis selected from the group comprising a stent strut or a guidewire. 28.The computer-readable, non-transitory medium of claim 27, wherein afeature is selected from the group consisting of the Cartesiancoordinates of a pixel, the intensity at each pixel, and the first andsecond order derivatives of the image in the x and y direction.
 29. Thecomputer-readable, non-transitory medium of claim 27, wherein thepre-defined intraluminal images and input intraluminal image areone-dimensional, two-dimensional, or three-dimensional.
 30. A method fordetecting an object in an intraluminal image, the method comprising thesteps of: generating a set of pre-defined intraluminal images known todisplay an object; computing principal components for the set to createan object space for the object; projecting an input intraluminal imageonto the object space; and detecting the object in the inputintraluminal image, wherein the object is selected from the groupcomprising a stent strut or a guidewire.
 31. The method of claim 30,further comprising processing the input intraluminal image.
 32. Themethod of claim 31, wherein said processing step processing comprisesremoving false object detections and highlighting the object detections.33. The method of claim 30, wherein the step of detecting the at leastone other object further comprises calculating an error between theinput intraluminal image and the object space for the object and betweenthe input intraluminal image and the object space for the at least oneother object.